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Buy AI compute only after you can describe the workload in numbers: model and precision, context and batch size, concurrency, latency or throughput target, expected utilization, scaling pattern, and tolerance for interruption. Those requirements determine memory capacity, interconnects, host design, storage, cloud terms, and total cost. A GPU name or peak specification by itself is not a purchase decision.
The practical process is to turn the workload into a system specification, shortlist compatible configurations, and validate the choice with the actual model and software before committing. The guidance and figures below come from official NVIDIA, Google Cloud, AWS, and Microsoft documentation; they describe listed capabilities and planning factors, not independent benchmark results or a universal recommendation.
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1. Write a workload specification before comparing hardware
Start with the service you must deliver, not with a preferred accelerator. Training and inference stress different parts of a system, and even two inference deployments of the same model can need very different memory because of context length and concurrency.
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Pretraining
Record the parameter count, training-token target, sequence length, global and per-device batch size, optimizer, precision, checkpoint interval, and target completion time. State whether the run must scale across nodes. Pretraining usually makes GPU-to-GPU and node-to-node communication, storage throughput, and restart behavior as important as raw tensor throughput.
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Fine-tuning
Specify the adaptation method, trainable parameter fraction, sequence length, batch size, precision, evaluation workload, and whether you will run one experiment or many concurrent experiments. Parameter-efficient methods can change memory pressure, but the saving depends on the implementation and optimizer. Plan a pilot with the exact framework rather than assuming a published peak-memory figure applies.
Batch inference
Define the number of items per batch, jobs per day, acceptable queue time, input and output sizes, and whether jobs can pause or be retried. Batch jobs can often tolerate lower instantaneous latency and can use interruptible capacity if checkpoints or idempotent job design protect progress.
Interactive serving
Write a service-level target for first-token latency, time per output token, requests per second, tail latency, and maximum concurrent requests. Include the normal and worst-case input and output context. KV-cache growth can make a deployment that fits at low concurrency fail when traffic rises.
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|---|---|---|
| Model | Parameter count, architecture, framework and version | Determines supported kernels, memory layout and parallelism options |
| Numerical format | FP32, FP16, BF16, FP8, INT8, INT4 or another tested format | Changes memory use and available compute paths; quality and throughput effects require model-specific validation |
| Input and output | Typical and maximum context, generated tokens, image or audio dimensions | Drives activation and KV-cache memory and affects latency |
| Concurrency or batch | Average, peak and target batch size | Sets aggregate memory demand and throughput requirements |
| Service target | Latency percentiles, throughput, completion time and utilization goal | Lets you compare systems on delivered work rather than peak specifications |
| Execution model | Single GPU, multi-GPU, multi-node, or elastic cloud jobs | Determines interconnect, RDMA, scheduler and storage requirements |
| Interruption tolerance | Can a run be preempted, and how much work may be lost? | Controls whether spot or other interruptible capacity is acceptable |
Google Cloud’s GPU guidance frames selection around computational characteristics, memory requirements, precision formats and scalability. Use the same categories in your requirements document so every quote answers the same question.
2. Size memory for the whole workload, not just the weights
Inference weight estimates
A quick first estimate is parameter count multiplied by bytes per parameter, followed by allowance for runtime overhead. AWS gives these approximate weight-only figures for a 70-billion-parameter model:
| 70B model format | Approximate weight storage | What the figure excludes |
|---|---|---|
| FP16 | 140 GB | KV cache, activations, framework allocations, temporary buffers and operating-system needs |
| FP8 or INT8 | 70 GB | KV cache, activations, framework allocations, temporary buffers and operating-system needs |
| INT4 or NVFP4 | 35 GB | KV cache, activations, framework allocations, temporary buffers and operating-system needs |
These are planning estimates, not guaranteed runtime requirements. AWS uses the 70B FP8 example to show that approximately 70 GB of weights exceeds the 48 GB HBM on one L40S. A deployment therefore needs sharding across GPUs or an accelerator with more HBM, such as an H100 or B200, subject to the model’s software support.
KV cache and runtime headroom
For autoregressive serving, the KV cache can become as large as, or larger than, the weights. Its approximate size rises with:
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- Concurrent requests or batch size.
- The number of layers, attention heads and KV heads.
- Head dimension.
- Cache precision.
Measure memory at the maximum concurrency and context you intend to advertise, not only at an average request. Leave headroom for the inference engine, communication buffers, tokenizer and application process. Quantization can reduce weight memory, but the cited guidance does not establish a universal quality or throughput trade-off; test the target model, quantization method and serving stack.
Training memory
Training requires space for model parameters, gradients, optimizer states, activations, communication buffers and checkpoints. Sequence length and batch size can raise activation memory sharply. Record whether activation checkpointing, sharding, offload or parameter-efficient training is part of the design, then verify the result in a pilot. The available documentation does not provide a universal training-memory calculator, so do not infer a guaranteed training fit from a GPU’s HBM number alone.
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3. Compare accelerator capabilities in the context of a system
Memory capacity determines whether a model fits; bandwidth and supported numerical formats influence how quickly data can move; the interconnect determines how efficiently multiple GPUs cooperate. Peak specifications are vendor figures and are not a substitute for a workload benchmark.
| Accelerator in NVIDIA’s HGX reference | Per-GPU memory | Memory bandwidth | Eight-GPU aggregate memory | Documented GPU-to-GPU bandwidth |
|---|---|---|---|---|
| H100 SXM | 80 GB HBM3 | 3.35 TB/s | 640 GB | 900 GB/s on HGX H100 |
| H200 SXM | 141 GB HBM3e | 4.8 TB/s | 1.1 TB | 900 GB/s on HGX H200 |
| B200 SXM | 180 GB HBM3e | Up to 8 TB/s | 1.44 TB | 1,800 GB/s on HGX B200 |
These values are specifications for the named HGX configurations verified in 2026, not independent comparisons of speed, price or efficiency. Confirm that the exact board, firmware, driver and server configuration supports the numerical formats and software features your model uses.
Why the fabric matters
Distributed training repeatedly exchanges gradients and other tensors. Slow links can leave expensive GPUs waiting at synchronization points. Microsoft’s Azure guidance states: "For training, select SKUs that support RDMA and GPU interconnects for high-speed data transfer between GPUs." Check GPU-to-GPU links inside a node, RDMA-capable networking between nodes, topology, adapter count and the scaling limit of the chosen software stack.
For inference, the requirement depends on the parallelism strategy. A single-GPU service may not need a cluster fabric, while a sharded model or high-concurrency deployment can depend on low-latency GPU links. Azure’s VM guidance says InfiniBand is unnecessary for inference in its described scenarios; treat that as an Azure-specific recommendation, not a rule for every serving architecture.
4. Specify the complete server around the accelerator
An accelerator is one component in a host, power and software system. Before accepting a quote, verify the following items in the exact chassis and configuration.
Host and memory
- Supported accelerator form factor, quantity and slot layout.
- CPU model and socket count sufficient for data loading, tokenization and orchestration.
- System RAM capacity and channels; large models may need substantial host staging memory even when weights reside in HBM.
- PCIe generation, lane allocation and NUMA placement.
- Network adapters, bus placement and driver support.
Local storage and data path
NVIDIA’s HGX reference lists up to 400 Gbps per network adapter, at least 1 TB of NVMe per CPU socket for inference servers, at least 2 TB per CPU socket for training or deep-learning servers, and a 1 TB boot drive. These are recommendations for that reference design, not universal minimums. Match capacity and throughput to dataset size, checkpoint frequency, image or video preprocessing and the number of simultaneous jobs. Confirm whether storage is local, network-attached or ephemeral, and measure the time to stage a dataset and write a checkpoint.
Power and cooling
Obtain the system’s sustained and peak power figures, input-voltage requirements, redundant power layout, airflow direction and cooling method. Compare them with rack power limits, facility cooling capacity and available circuits. A quote that lists GPUs but omits power distribution, cooling and service requirements is incomplete.
Example of integrated scale
NVIDIA’s DGX H100 user guide describes an integrated eight-GPU system with 640 GB total GPU memory; the DGX H200 guide lists 1,128 GB. Both describe 900 GB/s GPU-to-GPU bandwidth, 2 TB of system memory, NVMe storage and network adapters configurable for InfiniBand or Ethernet. The systems list six 3.3 kW power supplies. These figures illustrate the infrastructure that can accompany high-end data-center accelerators; they do not describe an unrelated workstation or a generic eight-GPU build.
5. Decide whether you need one GPU, one node or a cluster
Single-GPU deployment
Choose this path when the complete model and runtime fit in one device with headroom, latency targets are met, and the workload does not require distributed training. It simplifies operations and avoids communication overhead, but it may limit model size and future concurrency.
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Multi-GPU, single node
Use this when memory or throughput exceeds one GPU but the model can be sharded within a server. Confirm the actual GPU topology, peer-to-peer links, host NUMA layout and framework support. Aggregate HBM is not automatically one shared pool; the software must place and communicate tensors correctly.
Multi-node training
Require RDMA-capable networking, suitable node bandwidth, synchronized clocks and a storage path that keeps all workers fed. Define the maximum node count you will operate and test scaling efficiency at that count. A configuration that is fast on one node can deliver poor value if communication dominates at the planned scale.
6. Compare buying hardware with renting cloud capacity
Use the same completed workload in both scenarios. A useful comparison includes:
| Cost or operating factor | Owned infrastructure | Cloud capacity |
|---|---|---|
| Compute | Purchase price, financing, refresh and resale assumptions | Current hourly or committed rate for the exact instance |
| Utilization | Idle time between jobs and queueing across teams | Hours actually running, including startup and shutdown time |
| Facility | Power, cooling, rack space and building work | Usually embedded in the service rate, but verify included resources |
| Storage and network | Disks, shared storage, interconnects and maintenance | Attached storage, snapshots, data transfer and egress charges |
| Operations | Deployment, monitoring, patching, spare parts and support staff | Platform operations, quotas, images, drivers and support plan |
| Availability | Procurement lead time and hardware failure domain | Region capacity, quota, reservation terms and possible reclamation |
| Security and data | Physical control and internal compliance processes | Tenant controls, identity, encryption, persistence and egress policy |
Calculate cost per completed training run, processed batch or million generated tokens at the utilization you actually expect. Do not claim a break-even point from a GPU purchase price or an hourly rate alone; current quotes, region, workload duration and operating assumptions determine the result.
When ownership is sensible
Owned systems can fit a steady workload with high utilization, predictable capacity needs, data-residency constraints or a team prepared to operate power, cooling, hardware service and software. Include deployment time and the risk that a purchased configuration is underused or becomes difficult to refresh.
When renting is sensible
Cloud instances avoid buying a complete server and let you scale capacity for experiments, seasonal demand or uncertain model roadmaps. Check region and quota, actual availability, startup time, attached-storage limits, data-transfer charges, persistence, reservation terms and support for the required framework and drivers.
Spot and other interruptible capacity
Azure warns that spot capacity may be reclaimed at any time and recommends it only for workloads that can handle interruptions, with checkpointing. Apply that condition to any interruptible offer: quantify restart time, checkpoint frequency and the maximum acceptable lost work before calculating the apparent discount.
7. Review cloud offerings by configuration, not by product name
Google Cloud’s Compute Engine GPU documentation presents accelerator model, machine series, memory and bandwidth, GPU count, networking, local SSD and interconnect information together. Its guidance distinguishes frontier foundation-model training, which may need high Tensor Core throughput, large HBM capacity and high-speed NVLink, from fine-tuning and moderate training.
AWS currently documents EC2 P6e GB200 UltraServers and P6 B200 and B300 instances for AI training and inference. Verify the exact region, instance shape, quota, software image, storage attachment and pricing when you request a quote; a listing does not guarantee capacity for your schedule.
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Azure recommends GPU virtual machines for generative and complex-model training and inference, while noting that small non-generative workloads may fit CPU options. It recommends training SKUs with RDMA and GPU interconnects in its infrastructure guidance. These statements describe Azure’s documented choices and should not be generalized to every provider or model.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.8. Build a shortlist with explicit comparison axes
| Axis | Questions to answer | Evidence to request |
|---|---|---|
| Workload fit | Does the model, framework and precision path run on the proposed hardware? | Supported software versions and a model-specific pilot |
| Accelerator memory | Is there enough per-GPU and aggregate memory for weights, cache, activations and runtime headroom? | Measured peak allocation at maximum context and concurrency |
| Bandwidth and compute | Can the device sustain the required throughput at the chosen format? | Model-specific throughput and latency, not only peak figures |
| Scaling fabric | Are GPU links, RDMA, adapters and topology adequate at the planned scale? | Topology diagram and multi-GPU or multi-node scaling test |
| Host and storage | Can CPUs, RAM, NVMe and network adapters feed the accelerators? | Complete bill of materials and measured data-loading and checkpoint times |
| Capacity and operations | Can you obtain, deploy, monitor, patch and support the system when needed? | Lead time, quota, region availability, warranty and support terms |
| Total cost | What does one completed workload cost at expected utilization? | Quote with power, storage, network, labor and interruption assumptions |
9. Validate with a workload pilot before signing
- Freeze the test case. Use the production model revision, tokenizer, framework, driver and numerical format. Record representative and worst-case context lengths, output lengths and concurrency.
- Measure fit. Capture peak HBM, system RAM, temporary allocations and KV-cache growth. Confirm that the service remains within the planned headroom rather than merely starting successfully.
- Measure service behavior. Record first-token latency, steady-state token latency, throughput and tail percentiles at several concurrency levels. For batch or training, record examples or tokens completed per hour and checkpoint duration.
- Test scaling. Compare one, two and the planned maximum number of GPUs or nodes. Record utilization, synchronization time, network traffic and effective scaling efficiency.
- Test failure and recovery. Kill a worker, restart the job, restore a checkpoint and measure lost work and recovery time. For cloud capacity, test interruption handling and instance replacement.
- Test operations. Confirm image reproducibility, monitoring, logs, access controls, storage persistence, quota behavior and a clean teardown process.
- Set acceptance criteria. Reject a configuration that meets a peak number but misses latency, throughput, availability, recovery or cost targets under realistic conditions.
No independent comparative benchmark establishes that one of the named systems is a fixed multiple faster or cheaper for every workload. Your acceptance test is the evidence that should decide between otherwise compatible options.
10. Common purchasing mistakes and how to correct them
Buying by model name alone
Symptom: The accelerator is listed, but the quote omits chassis, power, cooling, network and software details. Correction: Require a complete system bill of materials and verify form factor, slot layout, adapters, firmware, drivers and support.
Sizing only for model weights
Symptom: The model loads at low concurrency but fails when context or traffic increases. Correction: Measure KV cache, activations and runtime allocations at maximum advertised conditions, then reserve headroom.
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Symptom: Several GPUs have enough total HBM on paper, but the model cannot be placed or communication is too slow. Correction: Validate sharding strategy, peer-to-peer topology and framework support on the exact node.
Ignoring data movement
Symptom: GPUs show low utilization while workers wait for input, checkpoints or synchronization. Correction: Measure storage throughput, data-loader behavior, GPU links and RDMA performance at the planned scale.
Using interruptible capacity without recovery engineering
Symptom: A reclaimed instance discards hours of work. Correction: Add frequent, tested checkpoints, idempotent job submission and a documented restart path before counting spot savings.
Comparing sticker prices
Symptom: The cheapest hourly rate or lowest purchase quote produces the highest cost per completed job. Correction: Compare time to completion, utilization, storage, transfer, power, labor, support and availability for the same workload.
11. Procurement checklist
- Workload type identified: pretraining, fine-tuning, batch inference or interactive serving.
- Model, framework, precision and software versions recorded.
- Typical and maximum context, output, batch and concurrency documented.
- Latency, throughput, completion-time and utilization targets written down.
- Weight, KV-cache, activation, optimizer and runtime memory estimates completed.
- Single-GPU, single-node or multi-node topology selected with a reason.
- GPU-to-GPU links, RDMA, node network and storage path verified.
- CPU, system RAM, NVMe, network adapters, power, cooling and rack requirements included.
- Cloud region, quota, availability, persistence, transfer, reservation and interruption terms checked, or owned-hardware lead time and support confirmed.
- Model-specific pilot completed with memory, latency, throughput, scaling and recovery measurements.
- Total cost calculated for the same completed workload under realistic utilization.
12. A practical decision rule
Choose the smallest configuration that passes the workload acceptance test with memory headroom, meets the service target at planned concurrency, scales as required, and has a credible operating and recovery plan. If two options pass, prefer the one with lower cost per completed workload and lower operational risk, not automatically the one with the highest peak specification. If neither passes, change the workload assumptions, model format, parallelism or service target before buying more hardware.
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